Well placement optimization plays a major role in an efficient recovery of hydrocarbon resources. Reservoir management decision-making relies heavily on an ability to determine optimal well locations that will yield the largest financial return. While manual determination of well locations based on common sense is still commonly used in the petroleum industry, reservoir management teams are beginning to appreciate the use of automatic optimization tools for well placement. However, determination of optimal well locations is challenging in a complex and heterogeneous system. First, in many instances, flow properties of the reservoir are not known with a reasonable degree of accuracy, making flow performance prediction difficult, and therefore management and economic analyses are misleading. Second, even if reservoir properties were known with a reasonable degree of accuracy, the objective function of well placement optimization, for example, a net present value (NPV) or recovery factor, is often complex, non-convex, and in many cases multi-modal, which makes the search for an optimal solution challenging. Third, the optimization problem space is often large and it, in fact, grows drastically with an increasing number of design variables. Thus, in large reservoirs where several production and injection wells are to be placed, the problem of finding an optimal configuration of wells can be very challenging.